Road vehicles � Human performance and state in the context of automated driving

The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation of the level of threat an obstacle poses in the performance metrics is described. A methodology to quantify the level of threat of an obstacle is presented in this regard. The approach involves simultaneously considering multiple stimulus parameters (that elicit responses from drivers), thereby not ignoring multivariate interactions. Human-likeness of ADS is a desirable characteristic as ADS share road infrastructure with humans. The described method can be used to develop human-like perception and motion planning modules of ADS. In this regard, performance metrics capable of quantifying human-likeness of ADS are also presented. A comparison of different performance metrics is then summarized. ADS operators have an obligation to report any incident (crash/disengagement) to safety regulating authorities. However, precrash events/states are not being reported. The need for the collection of the precrash scenario is described. A desirable modification to the data reporting/collecting is suggested as a framework. The framework describes the precrash sequences to be reported along with the possible ways of utilizing such a valuable dataset (by the safety regulating authorities) to comprehensively assess (and consequently improve) the safety of ADS. The framework proposes to collect and maintain a repository of precrash sequences. Such a repository can be used to 1) comprehensively learn and model the precrash scenarios, 2) learn the characteristics of precrash scenarios and eventually anticipate them, 3) assess the appropriateness of the different performance metrics in precrash scenarios, 4) synthesize a diverse dataset of precrash scenarios, 5) identify the ideal configuration of sensors and algorithms to enhance safety, and 6) monitor the performance of perception and motion planning modules.

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Road vehicles � Data communication between sensors and data fusion unit for automated driving functions � Logical interface

10.3403/30401255u ◽

2021 ◽

Keyword(s):

Data Fusion ◽

Data Communication ◽

Automated Driving ◽

Road Vehicles

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Reliable Road Scene Interpretation Based on ITOM with the Integrated Fusion of Vehicle and Lane Tracker in Dense Traffic Situation

Sensors ◽

10.3390/s20092457 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2457 ◽

Cited By ~ 1

Author(s):

Jinhan Jeong ◽

Yook Hyun Yoon ◽

Jahng Hyon Park

Keyword(s):

Feature Detection ◽

Lane Detection ◽

Experimental Result ◽

Automated Driving ◽

Road Vehicles ◽

Difficult Decision ◽

Research Areas ◽

Detection And Tracking ◽

Difficult Situations ◽

Systems Studies

Lane detection and tracking in a complex road environment is one of the most important research areas in highly automated driving systems. Studies on lane detection cover a variety of difficulties, such as shadowy situations, dimmed lane painting, and obstacles that prohibit lane feature detection. There are several hard cases in which lane candidate features are not easily extracted from image frames captured by a driving vehicle. We have carefully selected typical scenarios in which the extraction of lane candidate features can be easily corrupted by road vehicles and road markers that lead to degradations in the understanding of road scenes, resulting in difficult decision making. We have introduced two main contributions to the interpretation of road scenes in dense traffic environments. First, to obtain robust road scene understanding, we have designed a novel framework combining a lane tracker method integrated with a camera and a radar forward vehicle tracker system, which is especially useful in dense traffic situations. We have introduced an image template occupancy matching method with the integrated vehicle tracker that makes it possible to avoid extracting irrelevant lane features caused by forward target vehicles and road markers. Second, we present a robust multi-lane detection by a tracking algorithm that incudes adjacent lanes as well as ego lanes. We verify a comprehensive experimental evaluation with a real dataset comprised of problematic road scenarios. Experimental result shows that the proposed method is very reliable for multi-lane detection at the presented difficult situations.

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Environment representations for automated on-road vehicles

at - Automatisierungstechnik ◽

10.1515/auto-2017-0104 ◽

2018 ◽

Vol 66 (2) ◽

pp. 107-118 ◽

Cited By ~ 1

Author(s):

Matthias Schreier

Keyword(s):

Sensor Data ◽

Feature Maps ◽

Automated Driving ◽

Road Vehicles ◽

Interval Maps ◽

Digital Maps ◽

Occupancy Grid ◽

Abstraction Layer ◽

Environment Model ◽

Multi Level

AbstractOne of the key challenges of anyAutomated Driving(AD) system lies in the perception and representation of the driving environment. Data from a multitude of different information sources such as various vehicle environment sensors, external communication interfaces, and digital maps must be adequately combined to one consistentComprehensive Environment Model(CEM) that acts as a generic abstraction layer for the driving functions. This overview article summarizes and discusses different approaches in this area with a focus on metric representations of static and dynamic driving environments for on-road AD systems. Feature maps, parametric free space maps, interval maps, occupancy grid maps, elevation maps, the stixel world, multi-level surface maps, voxel grids, meshes, and raw sensor data models are presented and compared in this regard.

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Communications and Driver Monitoring Aids for Fostering SAE Level-4 Road Vehicles Automation

Electronics ◽

10.3390/electronics7100228 ◽

2018 ◽

Vol 7 (10) ◽

pp. 228 ◽

Cited By ~ 6

Author(s):

Felipe Jiménez ◽

José Naranjo ◽

Sofía Sánchez ◽

Francisco Serradilla ◽

Elisa Pérez ◽

...

Keyword(s):

Decision Making ◽

Cooperative Systems ◽

Automated Vehicles ◽

Automated Driving ◽

Road Vehicles ◽

Driver Monitoring ◽

Level 3 ◽

Assistance Systems ◽

Level 2

Road vehicles include more and more assistance systems that perform tasks to facilitate driving and make it safer and more efficient. However, the automated vehicles currently on the market do not exceed SAE level 2 and only in some cases reach level 3. Nevertheless, the qualitative and technological leap needed to reach level 4 is significant and numerous uncertainties remain. In this sense, a greater knowledge of the environment is needed for better decision making and the role of the driver changes substantially. This paper proposes the combination of cooperative systems with automated driving to offer a wider range of information to the vehicle than on-board sensors currently provide. This includes the actual deployment of a cooperative corridor on a highway. It also takes into account that in some circumstances or scenarios, pre-set or detected by on-board sensors or previous communications, the vehicle must hand back control to the driver, who may have been performing other tasks completely unrelated to supervising the driving. It is thus necessary to assess the driver’s condition as regards retaking control and to provide assistance for a safe transition.

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